Instructions to use lordx64/Qwable-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lordx64/Qwable-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lordx64/Qwable-v1-GGUF",
	filename="Qwable-v1.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lordx64/Qwable-v1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lordx64/Qwable-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lordx64/Qwable-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lordx64/Qwable-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lordx64/Qwable-v1-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf lordx64/Qwable-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lordx64/Qwable-v1-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf lordx64/Qwable-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lordx64/Qwable-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/lordx64/Qwable-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use lordx64/Qwable-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lordx64/Qwable-v1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lordx64/Qwable-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lordx64/Qwable-v1-GGUF:Q4_K_M

Ollama
How to use lordx64/Qwable-v1-GGUF with Ollama:
```
ollama run hf.co/lordx64/Qwable-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use lordx64/Qwable-v1-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lordx64/Qwable-v1-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for lordx64/Qwable-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lordx64/Qwable-v1-GGUF to start chatting

How to use lordx64/Qwable-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lordx64/Qwable-v1-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lordx64/Qwable-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lordx64/Qwable-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lordx64/Qwable-v1-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default lordx64/Qwable-v1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use lordx64/Qwable-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/lordx64/Qwable-v1-GGUF:Q4_K_M
```

Lemonade

How to use lordx64/Qwable-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lordx64/Qwable-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwable-v1-GGUF-Q4_K_M

List all available models

lemonade list

Qwable-v1-GGUF

File size: 1,647 Bytes

da65a67

---
base_model: lordx64/Qwable-v1
library_name: gguf
pipeline_tag: text-generation
tags:
- gguf
- llama.cpp
- lmstudio
- reasoning
- chain-of-thought
- qwen
- qwen3.6
- moe
- distillation
quantized_by: lordx64
license: apache-2.0
---

# Qwable-v1-GGUF

GGUF quantizations of [`lordx64/Qwable-v1`](https://huggingface.co/lordx64/Qwable-v1) for
use with [llama.cpp](https://github.com/ggerganov/llama.cpp) and
[LM Studio](https://lmstudio.ai/).

The base model is a reasoning-distilled variant of Qwen3.6-35B-A3B fine-tuned
to imitate the chain-of-thought style of Claude Opus 4.7. It thinks in explicit
`<think>...</think>` blocks before producing the final answer.

## Quant files

See the file list for all available quant levels. Common choices:

| File | Quant | Approx size | Use case |
|---|---|---|---|
| `*.IQ4_XS.gguf` | IQ4_XS | ~18 GB | Smallest quant with good quality — default pick for LM Studio |
| `*.Q4_K_M.gguf` | Q4_K_M | ~21 GB | Balanced quality / size |
| `*.Q5_K_M.gguf` | Q5_K_M | ~25 GB | Higher quality |
| `*.Q8_0.gguf`   | Q8_0   | ~35 GB | Near-lossless |

## Running in llama.cpp

```bash
llama-server \
  -m Qwable-v1.IQ4_XS.gguf \
  --host 127.0.0.1 --port 18081 \
  -c 32768 -fa on \
  --cache-type-k q8_0 --cache-type-v turbo4
```

## Running in LM Studio

Search for `lordx64/Qwable-v1-GGUF` inside LM Studio's model browser and pick the quant
that fits your RAM/VRAM. The model should appear automatically once HF indexes
this repo.

## License

Apache 2.0, inherited from the base model. See
[`lordx64/Qwable-v1`](https://huggingface.co/lordx64/Qwable-v1) for training details,
evaluations, and intended use.